OpenRaiser/Intern-Atlas huggingface dataset:
Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review
Venue: arXiv cs.CV 2016
Year: 2016

Video capsule endoscopy (VCE) is used widely nowadays for visualizing the gastrointestinal (GI) tract. Capsule endoscopy exams are prescribed usually as an additional monitoring mechanism and can help in identifying polyps, bleeding, etc. To analyze the large scale video data produced by VCE exams automatic image processing, computer vision, and learning algorithms are required. Recently, automatic polyp detection algorithms have been proposed with various degrees of success. Though polyp detection in colonoscopy and other traditional endoscopy procedure based images is becoming a mature field, due to its unique imaging characteristics detecting polyps automatically in VCE is a hard problem. We review different polyp detection approaches for VCE imagery and provide systematic analysis with challenges faced by standard image processing and computer vision methods.
Using Gaussian Processes for Rumour Stance Classification in Social Media
Venue: arXiv cs.CL 2016
Year: 2016

Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a rumourous conversation as either supporting, denying or questioning the rumour. Using a classifier based on Gaussian Processes, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will warn both ordinary users of Twitter and professional news practitioners when a rumour is being rebutted.
FairGA: Fair Genetic Algorithm - Beyond Resource-oriented Sustainability for ICT Products and Services
Venue: arXiv cs.CY 2016
Year: 2016

The complexity of ICT products and services has brought them to the level of disposable `species'. The combination of the race to optimal performance and disposability has resulted in considerable footprint and impact. Although approaches such as increasing efficiency, reducing the total cost of ownership, life cycle assessment and management, and circular economy have been put forward to manage and reduce the footprint and impact, the complexity of processes involved and especially invisibility of key but unobservable processes has resulted in some lower bounds for minimal achievable footprint. In this work, a modified approach to the Genetic Algorithm is proposed in order to introduce the notion of 'nondisposability' to the ICT products and services in order to implicitly influence and manage unobservable processes, and ultimately reduce the overall footprint and resource consumption. The proposed genetic algorithm is called FairGA, and it is compared with the traditional Genetic Algorithm against standard optimization functions. Also, the impact of the FairGA on the resource extraction has been illustrated with promising results.
Non-Evolutionary Superintelligences Do Nothing, Eventually
Venue: arXiv cs.AI 2016
Year: 2016

There is overwhelming evidence that human intelligence is a product of Darwinian evolution. Investigating the consequences of self-modification, and more precisely, the consequences of utility function self-modification, leads to the stronger claim that not only human, but any form of intelligence is ultimately only possible within evolutionary processes. Human-designed artificial intelligences can only remain stable until they discover how to manipulate their own utility function. By definition, a human designer cannot prevent a superhuman intelligence from modifying itself, even if protection mechanisms against this action are put in place. Without evolutionary pressure, sufficiently advanced artificial intelligences become inert by simplifying their own utility function. Within evolutionary processes, the implicit utility function is always reducible to persistence, and the control of superhuman intelligences embedded in evolutionary processes is not possible. Mechanisms against utility function self-modification are ultimately futile. Instead, scientific effort toward the mitigation of existential risks from the development of superintelligences should be in two directions: understanding consciousness, and the complex dynamics of evolutionary systems.
Sampling BSSRDFs with non-perpendicular incidence
Venue: arXiv cs.GR 2016
Year: 2016

Sub-surface scattering is key to our perception of translucent materials. Models based on diffusion theory are used to render such materials in a realistic manner by evaluating an approximation of the material BSSRDF at any two points of the surface. Under the assumption of perpendicular incidence, this BSSRDF approximation can be tabulated over 2 dimensions to provide fast evaluation and importance sampling. However, accounting for non-perpendicular incidence with the same approach would require to tabulate over 4 dimensions, making the model too large for practical applications. In this report, we present a method to efficiently evaluate and importance sample the multi-scattering component of diffusion based BSSRDFs for non-perpendicular incidence. Our approach is based on tabulating a compressed angular model of Photon Beam Diffusion. We explain how to generate, evaluate and sample our model. We show that 1 MiB is enough to store a model of the multi-scattering BSSRDF that is within $0.5\%$ relative error of Photon Beam Diffusion. Finally, we present a method to use our model in a Monte Carlo particle tracer and show results of our implementation in PBRT.
Pilot Contamination Attack Detection by Key-Confirmation in Secure MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

Many security techniques working at the physical layer need a correct channel state information (CSI) at the transmitter, especially when devices are equipped with multiple antennas. Therefore such techniques are vulnerable to pilot contamination attacks (PCAs) by which an attacker aims at inducing false CSI. In this paper we provide a solution to some PCA methods, by letting two legitimate parties to compare their channel estimates. The comparison is made in order to minimize the information leakage on the channel to a possible attacker. By reasonable assumptions on both the channel knowledge by the attacker and the correlation properties of the attacker and legitimate channels we show the validity of our solution. An accurate analysis of possible attacks and countermeasures is provided, together with a numerical evaluation of the attainable secrecy outage probability when our solution is used in conjunction with beamforming for secret communications.
Tighter bound of Sketched Generalized Matrix Approximation
Venue: arXiv cs.NA 2016
Year: 2016

Generalized matrix approximation plays a fundamental role in many machine learning problems, such as CUR decomposition, kernel approximation, and matrix low rank approximation. Especially with today's applications involved in larger and larger dataset, more and more efficient generalized matrix approximation algorithems become a crucially important research issue. In this paper, we find new sketching techniques to reduce the size of the original data matrix to develop new matrix approximation algorithms. Our results derive a much tighter bound for the approximation than previous works: we obtain a $(1+ε)$ approximation ratio with small sketched dimensions which implies a more efficient generalized matrix approximation.
Deformable Map Matching for Uncertain Loop-Less Maps
Venue: arXiv cs.RO 2016
Year: 2016

In the classical context of robotic mapping and localization, map matching is typically defined as the task of finding a rigid transformation (i.e., 3DOF rotation/translation on the 2D moving plane) that aligns the query and reference maps built by mobile robots. This definition is valid in loop-rich trajectories that enable a mapper robot to close many loops, for which precise maps can be assumed. The same cannot be said about the newly emerging autonomous navigation and driving systems, which typically operate in loop-less trajectories that have no large loop (e.g., straight paths). In this paper, we propose a solution that overcomes this limitation by merging the two maps. Our study is motivated by the observation that even when there is no large loop in either the query or reference map, many loops can often be obtained in the merged map. We add two new aspects to map matching: (1) image retrieval with discriminative deep convolutional neural network (DCNN) features, which efficiently generates a small number of good initial alignment hypotheses; and (2) map merge, which jointly deforms the two maps to minimize differences in shape between them. To realize practical computation time, we also present a preemption scheme that avoids excessive evaluation of useless map-matching hypotheses. To verify our approach experimentally, we created a novel collection of uncertain loop-less maps by utilizing the recently published North Campus Long-Term (NCLT) dataset and its ground-truth GPS data. The results obtained using these map collections confirm that our approach improves on previous map-matching approaches.
Learning Action Concept Trees and Semantic Alignment Networks from Image-Description Data
Venue: arXiv cs.CV 2016
Year: 2016

Action classification in still images has been a popular research topic in computer vision. Labelling large scale datasets for action classification requires tremendous manual work, which is hard to scale up. Besides, the action categories in such datasets are pre-defined and vocabularies are fixed. However humans may describe the same action with different phrases, which leads to the difficulty of vocabulary expansion for traditional fully-supervised methods. We observe that large amounts of images with sentence descriptions are readily available on the Internet. The sentence descriptions can be regarded as weak labels for the images, which contain rich information and could be used to learn flexible expressions of action categories. We propose a method to learn an Action Concept Tree (ACT) and an Action Semantic Alignment (ASA) model for classification from image-description data via a two-stage learning process. A new dataset for the task of learning actions from descriptions is built. Experimental results show that our method outperforms several baseline methods significantly.
Ear-to-ear Capture of Facial Intrinsics
Venue: arXiv cs.CV 2016
Year: 2016

We present a practical approach to capturing ear-to-ear face models comprising both 3D meshes and intrinsic textures (i.e. diffuse and specular albedo). Our approach is a hybrid of geometric and photometric methods and requires no geometric calibration. Photometric measurements made in a lightstage are used to estimate view dependent high resolution normal maps. We overcome the problem of having a single photometric viewpoint by capturing in multiple poses. We use uncalibrated multiview stereo to estimate a coarse base mesh to which the photometric views are registered. We propose a novel approach to robustly stitching surface normal and intrinsic texture data into a seamless, complete and highly detailed face model. The resulting relightable models provide photorealistic renderings in any view.
Waveform Candidates for 5G Networks: Analysis and Comparison
Venue: arXiv cs.IT 2016
Year: 2016

As the next generation cellular system, 5G network is required to provide a large variety of services for different kinds of terminals, from traditional voice and data services over mobile phones to small packet transmission over massive machine-type terminals. Although orthogonal-subcarrier based waveform has been widely used nowadays in many practical systems, it can hardly meet the future requirements in the coming 5G networks. Therefore, more flexible waveforms have been proposed to address the unprecedented challenges. In this article, we will provide comprehensive analysis and comparison for the typical waveform candidates. To obtain insightful analysis, we will not only introduce the basic principles of the waveforms but also reveal the underlying characteristics of each waveform. Moreover, a comprehensive comparison in terms of different performance metrics will be also presented in this article, which provide an overall understanding of the new waveforms.
On the Performance Analysis of Underlay Cognitive Radio Systems: A Deployment Perspective
Venue: arXiv cs.IT 2016
Year: 2016

We study the performance of cognitive Underlay System (US) that employ power control mechanism at the Secondary Transmitter (ST) from a deployment perspective. Existing baseline models considered for performance analysis either assume the knowledge of involved channels at the ST or retrieve this information by means of a band manager or a feedback channel, however, such situations rarely exist in practice. Motivated by this fact, we propose a novel approach that incorporates estimation of the involved channels at the ST, in order to characterize the performance of the US in terms of interference power received at the primary receiver and throughput at the secondary receiver (or \textit{secondary throughput}). Moreover, we apply an outage constraint that captures the impact of imperfect channel knowledge, particularly on the uncertain interference. Besides this, we employ a transmit power constraint at the ST to classify the operation of the US in terms of an interference-limited regime and a power-limited regime. In addition, we characterize the expressions of the uncertain interference and the secondary throughput for the case where the involved channels encounter Nakagami-$m$ fading. Finally, we investigate a fundamental tradeoff between the estimation time and the secondary throughput depicting an optimized performance of the US.
Interference-Free OFDM: Rethinking OFDM for Interference Networks with Inter-Symbol Interference
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers a $K$-user single-input-single-output interference channel with inter-symbol interference (ISI), in which the channel coefficients are assumed to be linear time-invariant with finite-length impulse response. The primary finding of this paper is that, with no channel state information at a transmitter (CSIT), the sum-spectral efficiency can be made to scale linearly with $K$, provided that the desired links have longer impulse response than do the interfering links. This linear gain is achieved by a novel multi-carrier communication scheme which we call \textit{interference-free orthogonal frequency division multiplexing (IF-OFDM)}. Furthermore, when a transmitter is able to learn CSIT from its paired receiver only, a higher sum-spectral efficiency can be achieved by a two-stage transmission method that concatenates IF-OFDM and vector coding based on singular value decomposition with water-filling power allocation. A major implication of the derived results is that separate encoding across subcarriers per link is sufficient to linearly increase the sum-spectral efficiency with $K$ in the interference channel with ISI. Simulation results support this claim.
Disintermediation of Inter-Blockchain Transactions
Venue: arXiv cs.CR 2016
Year: 2016

Different versions of peer-to-peer electronic cash exist as data represented by separate blockchains. Payments between such systems cannot be sent directly from one party to another without going through a financial institution. Bitcoin provided part of the solution but its utility is limited to intra-blockchain transactions. The benefits are lost if a trusted third party is required to execute inter-blockchain transactions. We propose a solution to the inter-blockchain transaction problem using the same fundamental principles of Bitcoin. The protocol is described by the Uberledger framework, a hierarchical meta-blockchain layer that encapsulates information regarding the fidelity of peer-to-peer transaction facilitators.
Robust Structure from Motion in the Presence of Outliers and Missing Data
Venue: arXiv cs.CV 2016
Year: 2016

Structure from motion is an import theme in computer vision. Although great progress has been made both in theory and applications, most of the algorithms only work for static scenes and rigid objects. In recent years, structure and motion recovery of non-rigid objects and dynamic scenes have received a lot of attention. In this paper, the state-of-the-art techniques for structure and motion factorization of non-rigid objects are reviewed and discussed. First, an introduction of the structure from motion problem is presented, followed by a general formulation of non-rigid structure from motion. Second, an augmented affined factorization framework, by using homogeneous representation, is presented to solve the registration issue in the presence of outlying and missing data. Third, based on the observation that the reprojection residuals of outliers are significantly larger than those of inliers, a robust factorization strategy with outlier rejection is proposed by means of the reprojection residuals, followed by some comparative experimental evaluations. Finally, some future research topics in non-rigid structure from motion are discussed.
O(f) Bi-Approximation for Capacitated Covering with Hard Capacities
Venue: arXiv cs.DM 2016
Year: 2016

We consider capacitated vertex cover with hard capacity constraints (VC-HC) on hypergraphs. In this problem we are given a hypergraph $G=(V,E)$ with a maximum edge size $f$. Each edge is associated with a demand and each vertex is associated with a weight (cost), a capacity, and an available multiplicity. The objective is to find a minimum-weight vertex multiset such that the demands of the edges can be covered by the capacities of the vertices and the multiplicity of each vertex does not exceed its available multiplicity. In this paper we present an $O(f)$ bi-approximation for VC-HC that gives a trade-off on the number of augmented multiplicity and the cost of the resulting cover. In particular, we show that, by augmenting the available multiplicity by a factor of $k \ge 2$, a~cover with a cost ratio of $\Big(1+\frac{1}{k-1}\Big)(f-1)$ to the optimal cover for the original instance can be obtained. This improves over a previous result, which has a cost ratio of $f^2$ via augmenting the available multiplicity by a factor of $f$.
An empirical study on the effects of different types of noise in image classification tasks
Venue: arXiv cs.CV 2016
Year: 2016

Image classification is one of the main research problems in computer vision and machine learning. Since in most real-world image classification applications there is no control over how the images are captured, it is necessary to consider the possibility that these images might be affected by noise (e.g. sensor noise in a low-quality surveillance camera). In this paper we analyse the impact of three different types of noise on descriptors extracted by two widely used feature extraction methods (LBP and HOG) and how denoising the images can help to mitigate this problem. We carry out experiments on two different datasets and consider several types of noise, noise levels, and denoising methods. Our results show that noise can hinder classification performance considerably and make classes harder to separate. Although denoising methods were not able to reach the same performance of the noise-free scenario, they improved classification results for noisy data.
Automated detection of smuggled high-risk security threats using Deep Learning
Venue: arXiv cs.CV 2016
Year: 2016

The security infrastructure is ill-equipped to detect and deter the smuggling of non-explosive devices that enable terror attacks such as those recently perpetrated in western Europe. The detection of so-called "small metallic threats" (SMTs) in cargo containers currently relies on statistical risk analysis, intelligence reports, and visual inspection of X-ray images by security officers. The latter is very slow and unreliable due to the difficulty of the task: objects potentially spanning less than 50 pixels have to be detected in images containing more than 2 million pixels against very complex and cluttered backgrounds. In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers. Novel approaches for dataset augmentation allowed to train CNNs from-scratch despite the scarcity of data available. We report fewer than 6% false alarms when detecting 90% SMTs synthetically concealed in stream-of-commerce images, which corresponds to an improvement of over an order of magnitude over conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers potentially super-human performance for a fraction of the time it would take for a security officers to carry out visual inspection (processing time is approximately 3.5s per container image).
Track Facial Points in Unconstrained Videos
Venue: arXiv cs.CV 2016
Year: 2016

Tracking Facial Points in unconstrained videos is challenging due to the non-rigid deformation that changes over time. In this paper, we propose to exploit incremental learning for person-specific alignment in wild conditions. Our approach takes advantage of part-based representation and cascade regression for robust and efficient alignment on each frame. Unlike existing methods that usually rely on models trained offline, we incrementally update the representation subspace and the cascade of regressors in a unified framework to achieve personalized modeling on the fly. To alleviate the drifting issue, the fitting results are evaluated using a deep neural network, where well-aligned faces are picked out to incrementally update the representation and fitting models. Both image and video datasets are employed to valid the proposed method. The results demonstrate the superior performance of our approach compared with existing approaches in terms of fitting accuracy and efficiency.
Android and Wireless data-extraction using Wi-Fi
Venue: arXiv cs.CR 2016
Year: 2016

Today, mobile phones are very popular, fast growing technology. Mobile phones of the present day are more and more like small computers. The so-called "smartphones" contain a wealth of information each. This information has been proven to be very useful in crime investigations, because relevant evidence can be found in data retrieved from mobile phones used by criminals. In traditional methods, the data from mobile phones can be extracted using an USB-cable. However, for some reason this USB-cable connection cannot be made, the data should be extracted in an alternative way. In this paper, we study the possibility of extracting data from mobile devices using a Wi-Fi connection. We describe our approach on mobile devices containing the Android Operating System. Through our experiments, we also give recommendation on which application and protocol can be used best to retrieve data.